An eHealth Intervention to Increase Physical Activity and Healthy Eating in Older Adult Cancer Survivors: Summative Evaluation Results
Bibliographic record
Abstract
BACKGROUND: A healthy lifestyle is associated with improved quality of life among cancer survivors, yet adherence to health behavior recommendations is low. OBJECTIVE: This pilot trial developed and tested the feasibility of a tailored eHealth program to increase fruit and vegetable consumption and physical activity among older, long-term cancer survivors. METHODS: American Cancer Society (ACS) guidelines for cancer survivors were translated into an interactive, tailored health behavior program on the basis of Social Cognitive Theory. Patients (N=86) with a history of breast (n=83) or prostate cancer (n=3) and less than 5 years from active treatment were randomized 1:1 to receive either provider advice, brief counseling, and the eHealth program (intervention) or advice and counseling alone (control). Primary outcomes were self-reported fruit and vegetable intake and physical activity. RESULTS: About half (52.7%, 86/163) of the eligible patients consented to participate. The most common refusal reasons were lack of perceived time for the study (32/163) and lack of interest in changing health behaviors (29/163). Furthermore, 72% (23/32) of the intervention group reported using the program and most would recommend it to others (56%, 14/25). Qualitative results indicated that the intervention was highly acceptable for survivors. For behavioral outcomes, the intervention group reported increased fruit and vegetable consumption. Self-reported physical activity declined in both groups. CONCLUSIONS: The brief intervention showed promising results for increasing fruit and vegetable intake. Results and participant feedback suggest that providing the intervention in a mobile format with greater frequency of contact and more indepth information would strengthen treatment effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".